The present embodiment relates to method and system for dynamically identifying the optimal servers from among a plurality of VPN servers. The method and system to score or rank the plurality of VPN servers through mathematical operations to produce a scored list of servers. The servers are dynamically scored based on several server conditions including but not limited to server location, server hub score, server creation time, server load, and other like information. The method and system further calculate server penalty scores for a plurality of VPN servers and dynamically identifies optimal servers based on the least server penalty score. Further, the method and system provide means for the VPN service provider to direct their users to connect with the optimal servers consistently.
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1. A method for identifying an optimal virtual private network (VPN) server from a plurality of VPN servers, the method comprising: receiving a request, at an application programming interface (API), from a user device remote to the API, to connect to an optimal VPN server from a plurality of VPN servers remote to the API and the user device; sending, from the API to a server picker infrastructure, a request for identifying the optimal VPN server for the user device; receiving, at the server picker infrastructure, from the API, the request for identifying the optimal VPN server for the user device from the plurality of VPN servers; receiving, at the server picker infrastructure, from a VPN server database, a plurality of VPN server conditions for each of the plurality of VPN servers; calculating, at the server picker infrastructure, a plurality of numerical weights corresponding to the conditions for each of the plurality of VPN servers; calculating, at the server picker infrastructure, a penalty score for each of the plurality of VPN servers based on the plurality of numerical weights; identifying, at the server picker infrastructure, based on the calculated penalty score, the optimal VPN server from the plurality of VPN servers; and sending, from the server picker infrastructure to the user device via the API, IP address of the optimal VPN server, wherein the plurality of numerical weights include at least two of country weight, hub weight, time weight or load weight, wherein the conditions for each of the plurality of VPN servers comprises at least one of a location, a hub score, a time of creation or a server load, wherein the country weight is calculated as being 1 if a VPN server from the plurality of VPN servers is in the same country as the user device, and wherein the country weight is 0 otherwise, and wherein the hub weight for the VPN server from the plurality of VPN servers is a product of the country weight and a hub score of the VPN server, wherein the hub score is a numerical value based on the proximity of the VPN server to an international Internet exchange hub.
This invention relates to a method for selecting an optimal virtual private network (VPN) server from a pool of available servers to enhance connection performance for a user. The method addresses the challenge of efficiently routing user traffic through the most suitable VPN server based on multiple factors, such as geographic proximity, network infrastructure quality, and server load. The process begins when a user device sends a request to an application programming interface (API) to connect to the best VPN server. The API forwards this request to a server picker infrastructure, which evaluates the available VPN servers. The server picker retrieves server conditions, including location, hub score, creation time, and load, from a VPN server database. It then calculates numerical weights for each server based on these conditions. The weights include country weight (1 if the server is in the same country as the user, 0 otherwise), hub weight (a product of the country weight and the server's hub score, which reflects proximity to an international Internet exchange hub), time weight, and load weight. Using these weights, the server picker computes a penalty score for each server. The server with the lowest penalty score is identified as the optimal choice. The server picker then sends the IP address of this optimal server back to the user device via the API, enabling a faster and more reliable VPN connection. This method ensures that users are automatically connected to the most efficient VPN server based on real-time conditions.
2. The system of claim 1 , wherein the load weight is 1 if a server load of a VPN server from the plurality of the VPN servers is greater than a predefined overload threshold, and wherein the load weight is 0 otherwise.
A system for managing server load in a virtual private network (VPN) environment includes a plurality of VPN servers and a load balancing mechanism that assigns a load weight to each server based on its current load. The load weight is set to 1 if the server's load exceeds a predefined overload threshold, indicating the server is overloaded, and 0 otherwise, indicating the server is operating within acceptable limits. This binary load weight assignment helps the system identify overloaded servers and distribute incoming traffic more efficiently to prevent performance degradation. The system dynamically adjusts load weights in real-time to ensure optimal resource utilization and maintain service reliability. The load balancing mechanism may further include additional criteria for distributing traffic, such as server response times, available bandwidth, or geographic proximity to users. By dynamically assigning load weights, the system ensures that overloaded servers are bypassed until their load decreases, thereby improving overall network performance and user experience.
4. The method of claim 1 , further comprising: generating a random value as a function of a random value seed generated using at least one of a server ID corresponding to each of the plurality of VPN servers, unix time, or client application ID, wherein the server ID is a numerical identity assigned to each VPN server of the plurality of VPN servers, the client application ID is a numerical identity assigned to an application at the user device, and unix time is the time on each VPN server of the plurality of VPN servers, and wherein the calculating the penalty score is further based on the random value.
This invention relates to a method for enhancing security in a virtual private network (VPN) system by dynamically calculating a penalty score to influence server selection. The problem addressed is ensuring secure and efficient routing of user traffic across multiple VPN servers while mitigating risks like server overload or predictable patterns in server selection. The method involves generating a random value as a function of a random value seed, which is derived from at least one of a server ID, Unix time, or a client application ID. The server ID is a numerical identifier assigned to each VPN server, the client application ID is a numerical identifier assigned to the user's application, and Unix time represents the current time on each VPN server. This random value is used as an additional factor in calculating a penalty score, which influences the selection of VPN servers for routing user traffic. The penalty score may also consider other factors, such as server load or historical usage, to optimize server selection while maintaining security and performance. By incorporating randomness into the penalty score calculation, the method reduces predictability in server selection, enhancing resistance to attacks and improving load distribution across the VPN infrastructure.
6. The method of claim 5 , wherein the VPN server having the lowest penalty score among the plurality of VPN servers is identified as the optimal VPN server.
A method for selecting an optimal VPN server from a plurality of VPN servers involves evaluating each server based on a penalty score. The penalty score is calculated by assessing various performance metrics, such as latency, bandwidth, connection stability, and server load. The VPN server with the lowest penalty score is identified as the optimal server for establishing a secure and efficient connection. This selection process ensures that the chosen VPN server provides the best performance for the user's needs, balancing factors like speed, reliability, and resource availability. The method may also include dynamically updating the penalty scores in real-time to adapt to changing network conditions, ensuring continuous optimization of VPN server selection. By prioritizing the server with the lowest penalty score, the method enhances user experience by minimizing connection delays and maximizing data transfer efficiency.
7. The method of claim 5 , further comprising: storing VPN server conditions for each of the plurality of VPN servers in the VPN server database; and updating the VPN server conditions on the VPN server database periodically or in real time.
A system and method for managing and optimizing virtual private network (VPN) server selection and routing involves dynamically assessing and updating VPN server conditions to improve network performance and reliability. The technology addresses the challenge of efficiently routing user traffic through optimal VPN servers, which can be affected by factors such as server load, latency, and availability. The system includes a VPN server database that stores conditions for multiple VPN servers, such as performance metrics, geographic locations, and operational status. These conditions are periodically or continuously updated to reflect real-time changes in server performance. The system evaluates these conditions to select the most suitable VPN server for a user's connection, ensuring efficient and reliable network routing. By dynamically adjusting server selection based on up-to-date conditions, the system enhances user experience and network efficiency. The method also involves monitoring server conditions to detect and mitigate potential issues, such as high latency or server failures, ensuring continuous optimization of VPN server performance.
8. The method of claim 7 , further comprising updating the penalty score for each of the plurality of VPN servers.
A system and method for managing and optimizing virtual private network (VPN) server performance involves monitoring and adjusting penalty scores assigned to VPN servers based on their operational metrics. The method includes collecting performance data from multiple VPN servers, such as latency, bandwidth usage, and connection stability, to assess their reliability and efficiency. A penalty score is calculated for each VPN server based on this data, where higher scores indicate poorer performance. The penalty scores are then used to prioritize server selection, ensuring that higher-performing servers are preferred for new connections. Additionally, the penalty scores are periodically updated to reflect ongoing performance trends, allowing the system to dynamically adapt to changing network conditions. This approach improves overall VPN service quality by reducing connection failures and optimizing resource allocation. The method may also involve load balancing techniques to distribute traffic evenly across servers, further enhancing system reliability. By continuously evaluating and adjusting server performance metrics, the system ensures efficient and reliable VPN connectivity for users.
10. The method of claim 1 , wherein a testing module collects VPN server condition from each of the plurality of VPN servers, and sends the VPN server condition to the server picker infrastructure.
A system monitors and selects optimal VPN servers for user connections. The problem addressed is ensuring reliable and efficient VPN server selection by dynamically assessing server conditions. The system includes a testing module that collects real-time performance data from multiple VPN servers, such as latency, bandwidth, and server load. This data is sent to a server picker infrastructure, which analyzes the conditions to determine the best server for a user's connection. The infrastructure then directs the user to the selected VPN server, improving connection quality and reliability. The testing module continuously monitors server conditions to ensure ongoing optimization. This approach enhances user experience by dynamically adapting to network changes and server performance fluctuations. The system is particularly useful for applications requiring stable and high-performance VPN connections, such as remote work, streaming, or secure data transfer.
11. A system for identifying an optimal virtual private network (VPN) server from a plurality of VPN servers, the system comprising: at least one processor; and a memory communicably coupled to the at least one processor, the memory comprising computer-executable instructions, which when executed by the at least one processor, performs a method comprising: receiving a request, at an application programming interface (API), from a user device remote to the API, to connect to an optimal VPN server from a plurality of VPN servers remote to the API and the user device, sending, from the API to a server picker infrastructure, a request for identifying the optimal VPN server for the user device, receiving, at the server picker infrastructure, from the API, the request for identifying the optimal VPN server for the user device from the plurality of VPN servers, receiving, at the server picker infrastructure, from a VPN server database, a plurality of VPN server conditions for each of the plurality of VPN servers, calculating, at the server picker infrastructure, a plurality of numerical weights corresponding to the conditions for each of the plurality of VPN servers, calculating, at the server picker infrastructure, a penalty score for each of the plurality of VPN servers based on the plurality of numerical weights, identifying, at the server picker infrastructure, based on the calculated penalty score, the optimal VPN server from the plurality of VPN servers, and sending, from the server picker infrastructure to the user device via the API, IP address of the optimal VPN server, wherein the plurality of numerical weights include at least two of country weight, hub weight, time weight or load weight, wherein the conditions for each of the plurality of VPN servers comprises at least one of a location, a hub score, a time of creation or a server load, wherein the country weight is calculated as being 1 if a VPN server from the plurality of VPN servers is in the same country as the user device, and wherein the country weight is 0 otherwise, and wherein the hub weight for the VPN server from the plurality of VPN servers is a product of the country weight and a hub score of the VPN server, wherein the hub score is a numerical value based on the proximity of the VPN server to an international Internet exchange hub.
This system identifies an optimal virtual private network (VPN) server from multiple available VPN servers for a user device. The system addresses the challenge of selecting the best VPN server based on various performance and location factors to ensure efficient and reliable connections. The system includes at least one processor and a memory storing executable instructions. When executed, the system receives a connection request from a user device via an application programming interface (API). The API forwards this request to a server picker infrastructure, which evaluates the available VPN servers. The server picker infrastructure retrieves VPN server conditions, such as location, hub score, time of creation, and server load, from a VPN server database. It then calculates numerical weights for each server based on these conditions, including country weight, hub weight, time weight, and load weight. The country weight is 1 if the VPN server is in the same country as the user device and 0 otherwise. The hub weight is the product of the country weight and the hub score, where the hub score reflects the server's proximity to an international Internet exchange hub. The system calculates a penalty score for each server using these weights and identifies the server with the lowest penalty score as the optimal choice. The IP address of the optimal server is then sent back to the user device via the API, enabling a seamless and efficient VPN connection.
12. The system of claim 11 , wherein the load weight is 1 if a server load of a VPN server from the plurality of the VPN servers is greater than a predefined overload threshold, and wherein the load weight is 0 otherwise.
This invention relates to a system for managing server load in a virtual private network (VPN) environment. The problem addressed is the need to efficiently distribute client connections across multiple VPN servers to prevent overload and ensure optimal performance. The system dynamically assigns load weights to VPN servers based on their current load levels. A server's load weight is set to 1 if its load exceeds a predefined overload threshold, indicating it is overburdened and should receive fewer new connections. Conversely, the load weight is set to 0 if the server's load is below the threshold, meaning it can accept additional connections. This mechanism helps balance the distribution of client requests, preventing any single server from becoming a bottleneck. The system likely integrates with a broader load-balancing framework that uses these weights to route new connections to the least loaded servers, thereby improving overall network efficiency and reliability. The predefined overload threshold is a configurable parameter that defines the maximum acceptable load for a server before it is considered overloaded. This approach ensures that VPN services remain responsive and scalable under varying traffic conditions.
14. The system of claim 11 , wherein the method further comprises: generating a random value as a function of a random value seed generated using at least one of a server ID corresponding to each of the plurality of VPN servers, unix time, or client application ID, wherein the server ID is a numerical identity assigned to each VPN server of the plurality of VPN servers, the client application ID is a numerical identity assigned to an application at the user device, and unix time is the time on each VPN server of the plurality of VPN servers, and wherein the calculating the penalty score is further based on the random value.
This invention relates to a system for enhancing security in a virtual private network (VPN) environment by dynamically calculating a penalty score to mitigate potential threats. The system includes multiple VPN servers, each assigned a unique numerical server ID, and user devices running client applications, each assigned a unique numerical client application ID. The system generates a random value as a function of a random value seed, which is derived from at least one of the server ID, Unix time (the current time on each VPN server), or the client application ID. This random value is used as an additional factor in calculating a penalty score, which assesses the risk or suspicious activity associated with a user's connection. By incorporating these dynamic variables, the system improves the accuracy and unpredictability of threat detection, making it harder for malicious actors to bypass security measures. The use of server IDs, Unix time, and client application IDs ensures that the random value is unique and time-sensitive, further enhancing security. This approach helps prevent unauthorized access and ensures secure communication within the VPN network.
17. The system of claim 11 , further comprising a testing module configured to collect VPN server condition from each of the plurality of VPN servers, and send the VPN server condition to the server picker infrastructure.
A system for managing and optimizing VPN server selection includes a testing module that monitors the operational status and performance conditions of multiple VPN servers. The testing module collects real-time data on each VPN server's condition, such as latency, bandwidth, uptime, and security status, and transmits this information to a central server picker infrastructure. The server picker infrastructure uses this data to dynamically select the most suitable VPN server for a user's connection request, ensuring optimal performance and reliability. The system may also include a user interface for displaying available VPN servers and their conditions, allowing users to manually select a server if desired. The testing module continuously updates the server picker infrastructure with the latest VPN server conditions to maintain accurate and up-to-date server selection decisions. This system improves VPN service quality by dynamically adapting to changing network conditions and server performance.
18. A non-transitory computer readable medium (CRM) comprising computer-executable instructions, which when executed by a processor perform a method for identifying an optimal virtual private network (VPN) server from a plurality of VPN servers, the method comprising: receiving a request, at an application programming interface (API), from a user device remote to the API, to connect to an optimal VPN server from a plurality of VPN servers remote to the API and the user device; sending, from the API to a server picker infrastructure, a request for identifying the optimal VPN server for the user device; receiving, at the server picker infrastructure, from the API, the request for identifying the optimal VPN server for the user device from the plurality of VPN servers; receiving, at the server picker infrastructure, from a VPN server database, a plurality of VPN server conditions for each of the plurality of VPN servers; calculating, at the server picker infrastructure, a plurality of numerical weights corresponding to the conditions for each of the plurality of VPN servers; calculating, at the server picker infrastructure, a penalty score for each of the plurality of VPN servers based on the plurality of numerical weights; identifying, at the server picker infrastructure, based on the calculated penalty score, the optimal VPN server from the plurality of VPN servers; and sending, from the server picker infrastructure to the user device via the API, IP address of the optimal VPN server, wherein the plurality of numerical weights include at least two of country weight, hub weight, time weight or load weight, wherein the conditions for each of the plurality of VPN servers comprises at least one of a location, a hub score, a time of creation or a server load, wherein the country weight is calculated as being 1 if a VPN server from the plurality of VPN servers is in the same country as the user device, and wherein the country weight is 0 otherwise, and wherein the hub weight for the VPN server from the plurality of VPN servers is a product of the country weight and a hub score of the VPN server, wherein the hub score is a numerical value based on the proximity of the VPN server to an international Internet exchange hub.
A system for selecting an optimal virtual private network (VPN) server from multiple available servers evaluates server conditions to determine the best connection for a user. The system includes a server picker infrastructure that receives a request from a user device via an application programming interface (API). The infrastructure retrieves VPN server conditions, such as location, hub score, time of creation, and server load, from a VPN server database. These conditions are used to calculate numerical weights for each server, including country weight, hub weight, time weight, and load weight. The country weight is 1 if the server is in the same country as the user device and 0 otherwise. The hub weight is the product of the country weight and the server's hub score, which reflects its proximity to an international Internet exchange hub. The system calculates a penalty score for each server based on these weights and selects the server with the lowest penalty score as the optimal VPN server. The IP address of the selected server is then sent to the user device via the API. This approach ensures efficient and optimized VPN server selection based on multiple factors.
19. The CRM of claim 18 , further comprising: generating a random value as a function of a random value seed generated using at least one of a server ID corresponding to each of the plurality of VPN servers, unix time, or client application ID, wherein the server ID is a numerical identity assigned to each VPN server of the plurality of VPN servers, the client application ID is a numerical identity assigned to an application at the user device, and unix time is the time on each VPN server of the plurality of VPN servers, and wherein the calculating the penalty score is further based on the random value.
A system for managing customer relationships (CRM) includes a method for calculating a penalty score to assess the performance of a virtual private network (VPN) server. The system involves multiple VPN servers and a user device running a client application. The penalty score is determined based on various factors, including network latency, packet loss, and other performance metrics. To enhance security and unpredictability, the system generates a random value that influences the penalty score calculation. This random value is derived from a seed, which is generated using at least one of a server ID, Unix time, or a client application ID. The server ID is a unique numerical identifier assigned to each VPN server, the Unix time represents the current time on each VPN server, and the client application ID is a numerical identifier assigned to the application on the user device. By incorporating these elements, the system ensures that the penalty score calculation is both dynamic and resistant to manipulation, improving the accuracy and fairness of VPN server performance assessments.
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December 17, 2020
February 8, 2022
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